import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pytest

from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex

from .utils import require_elasticsearch, require_faiss


__a    :	Tuple         =    pytest.mark.integration




@require_faiss
class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Dict:

							'''simple docstring'''


							__lowercase					     =				Dataset.from_dict({'''filename''': ['''my_name-train''' + '''_''' + str(lowerCAmelCase__		) for x in np.arange(30		).tolist()]}		)
							return dset





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   List[Any]:

							'''simple docstring'''


							import faiss

							__lowercase					     =				self._create_dummy_dataset()
							__lowercase					     =				dset.map(
							    lambda lowerCAmelCase__ ,  lowerCAmelCase__		: {"vecs": i * np.ones(5 ,  dtype=np.floataa		)} ,  with_indices=lowerCAmelCase__ ,  keep_in_memory=lowerCAmelCase__		)
							__lowercase					     =				dset.add_faiss_index('''vecs''' ,  batch_size=1_00 ,  metric_type=faiss.METRIC_INNER_PRODUCT		)
							__lowercase     ,	__lowercase					     =				dset.get_nearest_examples('''vecs''' ,  np.ones(5 ,  dtype=np.floataa		)		)
							self.assertEqual(examples['''filename'''][0] ,  '''my_name-train_29'''		)
							dset.drop_index('''vecs'''		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   List[Any]:

							'''simple docstring'''


							import faiss

							__lowercase					     =				self._create_dummy_dataset()
							dset.add_faiss_index_from_external_arrays(
							    external_arrays=np.ones((30, 5)		) * np.arange(30		).reshape(-1 ,  1		) ,  index_name='''vecs''' ,  batch_size=1_00 ,  metric_type=faiss.METRIC_INNER_PRODUCT ,  )
							__lowercase     ,	__lowercase					     =				dset.get_nearest_examples('''vecs''' ,  np.ones(5 ,  dtype=np.floataa		)		)
							self.assertEqual(examples['''filename'''][0] ,  '''my_name-train_29'''		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   List[str]:

							'''simple docstring'''


							import faiss

							__lowercase					     =				self._create_dummy_dataset()
							dset.add_faiss_index_from_external_arrays(
							    external_arrays=np.ones((30, 5)		) * np.arange(30		).reshape(-1 ,  1		) ,  index_name='''vecs''' ,  metric_type=faiss.METRIC_INNER_PRODUCT ,  )

							# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
							# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
							# see https://bugs.python.org/issue14243 and
							# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
							with tempfile.NamedTemporaryFile(delete=lowerCAmelCase__		) as tmp_file:
											dset.save_faiss_index('''vecs''' ,  tmp_file.name		)
											dset.load_faiss_index('''vecs2''' ,  tmp_file.name		)
							os.unlink(tmp_file.name		)

							__lowercase     ,	__lowercase					     =				dset.get_nearest_examples('''vecs2''' ,  np.ones(5 ,  dtype=np.floataa		)		)
							self.assertEqual(examples['''filename'''][0] ,  '''my_name-train_29'''		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   str:

							'''simple docstring'''


							__lowercase					     =				self._create_dummy_dataset()
							dset.add_faiss_index_from_external_arrays(
							    external_arrays=np.ones((30, 5)		) * np.arange(30		).reshape(-1 ,  1		) ,  index_name='''vecs'''		)
							dset.drop_index('''vecs'''		)
							self.assertRaises(lowerCAmelCase__ ,  partial(dset.get_nearest_examples ,  '''vecs2''' ,  np.ones(5 ,  dtype=np.floataa		)		)		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   List[str]:

							'''simple docstring'''


							from elasticsearch import Elasticsearch

							__lowercase					     =				self._create_dummy_dataset()
							with patch('''elasticsearch.Elasticsearch.search'''		) as mocked_search, patch(
							    '''elasticsearch.client.IndicesClient.create'''		) as mocked_index_create, patch('''elasticsearch.helpers.streaming_bulk'''		) as mocked_bulk:
											__lowercase					     =				{'''acknowledged''': True}
											mocked_bulk.return_value([(True, None)] * 30		)
											__lowercase					     =				{'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 29}]}}
											__lowercase					     =				Elasticsearch()

											dset.add_elasticsearch_index('''filename''' ,  es_client=lowerCAmelCase__		)
											__lowercase     ,	__lowercase					     =				dset.get_nearest_examples('''filename''' ,  '''my_name-train_29'''		)
											self.assertEqual(examples['''filename'''][0] ,  '''my_name-train_29'''		)




@require_faiss
class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Any:

							'''simple docstring'''


							import faiss

							__lowercase					     =				FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT		)

							# add vectors
							index.add_vectors(np.eye(5 ,  dtype=np.floataa		)		)
							self.assertIsNotNone(index.faiss_index		)
							self.assertEqual(index.faiss_index.ntotal ,  5		)
							index.add_vectors(np.zeros((5, 5) ,  dtype=np.floataa		)		)
							self.assertEqual(index.faiss_index.ntotal ,  10		)

							# single query
							__lowercase					     =				np.zeros(5 ,  dtype=np.floataa		)
							__lowercase					     =				1
							__lowercase     ,	__lowercase					     =				index.search(lowerCAmelCase__		)
							self.assertRaises(lowerCAmelCase__ ,  index.search ,  query.reshape(-1 ,  1		)		)
							self.assertGreater(scores[0] ,  0		)
							self.assertEqual(indices[0] ,  1		)

							# batched queries
							__lowercase					     =				np.eye(5 ,  dtype=np.floataa		)[::-1]
							__lowercase     ,	__lowercase					     =				index.search_batch(lowerCAmelCase__		)
							self.assertRaises(lowerCAmelCase__ ,  index.search_batch ,  queries[0]		)
							__lowercase					     =				[scores[0] for scores in total_scores]
							__lowercase					     =				[indices[0] for indices in total_indices]
							self.assertGreater(np.min(lowerCAmelCase__		) ,  0		)
							self.assertListEqual([4, 3, 2, 1, 0] ,  lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Tuple:

							'''simple docstring'''


							import faiss

							__lowercase					     =				FaissIndex(string_factory='''Flat'''		)
							index.add_vectors(np.eye(5 ,  dtype=np.floataa		)		)
							self.assertIsInstance(index.faiss_index ,  faiss.IndexFlat		)
							__lowercase					     =				FaissIndex(string_factory='''LSH'''		)
							index.add_vectors(np.eye(5 ,  dtype=np.floataa		)		)
							self.assertIsInstance(index.faiss_index ,  faiss.IndexLSH		)
							with self.assertRaises(lowerCAmelCase__		):
											__lowercase					     =				FaissIndex(string_factory='''Flat''' ,  custom_index=faiss.IndexFlat(5		)		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   List[Any]:

							'''simple docstring'''


							import faiss

							__lowercase					     =				faiss.IndexFlat(5		)
							__lowercase					     =				FaissIndex(custom_index=lowerCAmelCase__		)
							index.add_vectors(np.eye(5 ,  dtype=np.floataa		)		)
							self.assertIsInstance(index.faiss_index ,  faiss.IndexFlat		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Optional[int]:

							'''simple docstring'''


							import faiss

							__lowercase					     =				FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT		)
							index.add_vectors(np.eye(5 ,  dtype=np.floataa		)		)

							# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
							# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
							# see https://bugs.python.org/issue14243 and
							# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
							with tempfile.NamedTemporaryFile(delete=lowerCAmelCase__		) as tmp_file:
											index.save(tmp_file.name		)
											__lowercase					     =				FaissIndex.load(tmp_file.name		)
							os.unlink(tmp_file.name		)

							__lowercase					     =				np.zeros(5 ,  dtype=np.floataa		)
							__lowercase					     =				1
							__lowercase     ,	__lowercase					     =				index.search(lowerCAmelCase__		)
							self.assertGreater(scores[0] ,  0		)
							self.assertEqual(indices[0] ,  1		)





@require_faiss
def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				import faiss

				__lowercase					     =				FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT				)
				index.add_vectors(np.eye(5				,     dtype=np.floataa				)				)

				__lowercase					     =				'''index.faiss'''
				__lowercase					     =				F"mock://{index_name}"
				index.save(lowercase				,     storage_options=mockfs.storage_options				)
				__lowercase					     =				FaissIndex.load(lowercase				,     storage_options=mockfs.storage_options				)

				__lowercase					     =				np.zeros(5				,     dtype=np.floataa				)
				__lowercase					     =				1
				__lowercase     ,	__lowercase					     =				index.search(lowercase				)
				assert scores[0] > 0
				assert indices[0] == 1




@require_elasticsearch
class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   str:

							'''simple docstring'''


							from elasticsearch import Elasticsearch

							with patch('''elasticsearch.Elasticsearch.search'''		) as mocked_search, patch(
							    '''elasticsearch.client.IndicesClient.create'''		) as mocked_index_create, patch('''elasticsearch.helpers.streaming_bulk'''		) as mocked_bulk:
											__lowercase					     =				Elasticsearch()
											__lowercase					     =				{'''acknowledged''': True}
											__lowercase					     =				ElasticSearchIndex(es_client=lowerCAmelCase__		)
											mocked_bulk.return_value([(True, None)] * 3		)
											index.add_documents(['''foo''', '''bar''', '''foobar''']		)

											# single query
											__lowercase					     =				'''foo'''
											__lowercase					     =				{'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 0}]}}
											__lowercase     ,	__lowercase					     =				index.search(lowerCAmelCase__		)
											self.assertEqual(scores[0] ,  1		)
											self.assertEqual(indices[0] ,  0		)

											# single query with timeout
											__lowercase					     =				'''foo'''
											__lowercase					     =				{'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 0}]}}
											__lowercase     ,	__lowercase					     =				index.search(lowerCAmelCase__ ,  request_timeout=30		)
											self.assertEqual(scores[0] ,  1		)
											self.assertEqual(indices[0] ,  0		)

											# batched queries
											__lowercase					     =				['''foo''', '''bar''', '''foobar''']
											__lowercase					     =				{'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 1}]}}
											__lowercase     ,	__lowercase					     =				index.search_batch(lowerCAmelCase__		)
											__lowercase					     =				[scores[0] for scores in total_scores]
											__lowercase					     =				[indices[0] for indices in total_indices]
											self.assertGreater(np.min(lowerCAmelCase__		) ,  0		)
											self.assertListEqual([1, 1, 1] ,  lowerCAmelCase__		)

											# batched queries with timeout
											__lowercase					     =				['''foo''', '''bar''', '''foobar''']
											__lowercase					     =				{'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 1}]}}
											__lowercase     ,	__lowercase					     =				index.search_batch(lowerCAmelCase__ ,  request_timeout=30		)
											__lowercase					     =				[scores[0] for scores in total_scores]
											__lowercase					     =				[indices[0] for indices in total_indices]
											self.assertGreater(np.min(lowerCAmelCase__		) ,  0		)
											self.assertListEqual([1, 1, 1] ,  lowerCAmelCase__		)
import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__a    :	Union[str, Any]         =    1_6
__a    :	int         =    3_2





def 							UpperCAmelCase		(       lowercase				,     lowercase = 16				):







				"""simple docstring"""



				__lowercase					     =				AutoTokenizer.from_pretrained('''bert-base-cased'''				)
				__lowercase					     =				load_dataset('''glue'''				,     '''mrpc'''				)

				def tokenize_function(lowercase				):
								# max_length=None => use the model max length (it's actually the default)
								__lowercase					     =				tokenizer(examples['''sentence1''']				,     examples['''sentence2''']				,     truncation=lowercase				,     max_length=lowercase				)
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				# starting with the main process first:
				with accelerator.main_process_first():
								__lowercase					     =				datasets.map(
								    lowercase				,     batched=lowercase				,     remove_columns=['''idx''', '''sentence1''', '''sentence2''']				,     )

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				__lowercase					     =				tokenized_datasets.rename_column('''label'''				,     '''labels'''				)

				def collate_fn(lowercase				):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								__lowercase					     =				128 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												__lowercase					     =				16
								elif accelerator.mixed_precision != "no":
												__lowercase					     =				8
								else:
												__lowercase					     =				None

								return tokenizer.pad(
								    lowercase				,     padding='''longest'''				,     max_length=lowercase				,     pad_to_multiple_of=lowercase				,     return_tensors='''pt'''				,     )

				# Instantiate dataloaders.
				__lowercase					     =				DataLoader(
				    tokenized_datasets['''train''']				,     shuffle=lowercase				,     collate_fn=lowercase				,     batch_size=lowercase				)
				__lowercase					     =				DataLoader(
				    tokenized_datasets['''validation''']				,     shuffle=lowercase				,     collate_fn=lowercase				,     batch_size=lowercase				)

				return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
		from accelerate.test_utils.training import mocked_dataloaders

		__a    :	List[Any]         =    mocked_dataloaders  # noqa: F811





def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				if os.environ.get('''TESTING_MOCKED_DATALOADERS'''				,     lowercase				) == "1":
								__lowercase					     =				2
				# Initialize accelerator
				__lowercase					     =				Accelerator(cpu=args.cpu				,     mixed_precision=args.mixed_precision				)
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				__lowercase					     =				config['''lr''']
				__lowercase					     =				int(config['''num_epochs''']				)
				__lowercase					     =				int(config['''seed''']				)
				__lowercase					     =				int(config['''batch_size''']				)

				__lowercase					     =				evaluate.load('''glue'''				,     '''mrpc'''				)

				# If the batch size is too big we use gradient accumulation
				__lowercase					     =				1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								__lowercase					     =				batch_size // MAX_GPU_BATCH_SIZE
								__lowercase					     =				MAX_GPU_BATCH_SIZE

				set_seed(lowercase				)
				__lowercase     ,	__lowercase					     =				get_dataloaders(lowercase				,     lowercase				)
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				__lowercase					     =				AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''				,     return_dict=lowercase				)

				# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
				# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
				# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
				__lowercase					     =				model.to(accelerator.device				)

				# Instantiate optimizer
				__lowercase					     =				AdamW(params=model.parameters()				,     lr=lowercase				)

				# Instantiate scheduler
				__lowercase					     =				get_linear_schedule_with_warmup(
				    optimizer=lowercase				,     num_warmup_steps=100				,     num_training_steps=(len(lowercase				) * num_epochs) // gradient_accumulation_steps				,     )

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				__lowercase     ,	__lowercase     ,	__lowercase     ,	__lowercase     ,	__lowercase					     =				accelerator.prepare(
				    lowercase				,     lowercase				,     lowercase				,     lowercase				,     lowercase				)

				# Now we train the model
				for epoch in range(lowercase				):
								model.train()
								for step, batch in enumerate(lowercase				):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device				)
												__lowercase					     =				model(**lowercase				)
												__lowercase					     =				outputs.loss
												__lowercase					     =				loss / gradient_accumulation_steps
												accelerator.backward(lowercase				)
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

								model.eval()
								__lowercase					     =				0
								for step, batch in enumerate(lowercase				):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device				)
												with torch.no_grad():
																__lowercase					     =				model(**lowercase				)
												__lowercase					     =				outputs.logits.argmax(dim=-1				)
												__lowercase     ,	__lowercase					     =				accelerator.gather((predictions, batch['''labels'''])				)
												# New Code #
												# First we check if it's a distributed system
												if accelerator.use_distributed:
																# Then see if we're on the last batch of our eval dataloader
																if step == len(lowercase				) - 1:
																				# Last batch needs to be truncated on distributed systems as it contains additional samples
																				__lowercase					     =				predictions[: len(eval_dataloader.dataset				) - samples_seen]
																				__lowercase					     =				references[: len(eval_dataloader.dataset				) - samples_seen]
																else:
																				# Otherwise we add the number of samples seen
																				samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
												metric.add_batch(
												    predictions=lowercase				,     references=lowercase				,     )

								__lowercase					     =				metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F"epoch {epoch}:"				,     lowercase				)





def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				__lowercase					     =				argparse.ArgumentParser(description='''Simple example of training script.'''				)
				parser.add_argument(
				    '''--mixed_precision'''				,     type=lowercase				,     default=lowercase				,     choices=['''no''', '''fp16''', '''bf16''', '''fp8''']				,     help='''Whether to use mixed precision. Choose'''
				    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
				    '''and an Nvidia Ampere GPU.'''				,     )
				parser.add_argument('''--cpu'''				,     action='''store_true'''				,     help='''If passed, will train on the CPU.'''				)
				__lowercase					     =				parser.parse_args()
				__lowercase					     =				{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(lowercase				,     lowercase				)


if __name__ == "__main__":
		main()